List of Big Data Tutorials For The Beginners

At this moment, we lack a separate category for Big Data. At worst, hackers hacked our Twitter account and deleted 3000 tweets. Here is a list of Big Data tutorials we published so far. This list cum guide is intended for the beginners, so that they can install and configure the common softwares like Apache Hadoop on cloud server. We also have guides to find good yet cheap cloud servers, dedicated servers.

 

List of Big Data tutorials

 

We will keep this webpage updated. Use cases for large datasets can be for works like distributed processing. Processing data, whether in batch or real time streams is scalable and effective on top of a good platform. Using free softwares such as Spark, YARN, or Hive, businesses scale up to thousands of processing nodes using our API and control panel.

Reading these guides on can get used with the basics. Later can set up own database and storage platform for running large-scale databases or distributed in-memory caches, scale HDFS and deploy popular databases, such as Cassandra, MongoDB, Redis, or Presto.

Popular free distributed message brokers, like Kafka, can be used for event-driven applications, website activity tracking, or to feed into your Hadoop cluster. These highly scalable and fault-tolerant services depends on quality of hardware and network latency.

Interactively visualize, query, and perform analysis against your time series and metric databases using tools like Jupyter, Grafana, and Kibana. Kibana is an analytics and visualization platform that builds on Elasticsearch to give a better understanding of data. We have covered how to create searches, visualizations, and dashboards.

 

1. Install Apache Hadoop on Ubuntu on Single Cloud Server Instance
2. Install Apache Spark on Ubuntu Single Cloud Server With Hadoop
3. Automated Deployment of Apache Hadoop & Big Data Softwares
4. List of Apache Projects For Big Data
5. How to Install, Configure Elasticsearch with Apache Hadoop

Tagged With 6RSS

This Article Has Been Shared 920 Times!

Facebook Twitter Pinterest